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Carbogen-induced increases in tumor oxygenation depend on the vascular status of the tumor: A multiparametric MRI study in two rat glioblastoma models.

ABSTRACT: The alleviation of hypoxia in glioblastoma with carbogen to improve treatment has met with limited success. Our hypothesis is that the eventual benefits of carbogen depend on the capacity for vasodilation. We examined, with MRI, changes in fractional cerebral blood volume, blood oxygen saturation, and blood oxygenation level dependent signals in response to carbogen. The analyses were performed in two xenograft models of glioma (U87 and U251) recognized to have different vascular patterns. Carbogen increased fractional cerebral blood volume, blood oxygen saturation, and blood oxygenation level dependent signals in contralateral tissues. In the tumor core and peritumoral regions, changes were dependent on the capacity to vasodilate rather than on resting fractional cerebral blood volume. In the highly vascularised U87 tumor, carbogen induced a greater increase in fractional cerebral blood volume and blood oxygen saturation in comparison to the less vascularized U251 tumor. The blood oxygenation level dependent signal revealed a delayed response in U251 tumors relative to the contralateral tissue. Additionally, we highlight the considerable heterogeneity of fractional cerebral blood volume, blood oxygen saturation, and blood oxygenation level dependent within U251 tumor in which multiple compartments co-exist (tumor core, rim and peritumoral regions). Finally, our study underlines the complexity of the flow/metabolism interactions in different models of glioblastoma. These irregularities should be taken into account in order to palliate intratumoral hypoxia in clinical trials.

SUBMITTER: Chakhoyan A

PROVIDER: S-EPMC5464716 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Carbogen-induced increases in tumor oxygenation depend on the vascular status of the tumor: A multiparametric MRI study in two rat glioblastoma models.

Chakhoyan Ararat A Corroyer-Dulmont Aurélien A Leblond Marine M MM Gérault Aurélie A Toutain Jérôme J Chazaviel Laurent L Divoux Didier D Petit Edwige E MacKenzie Eric T ET Kauffmann François F Delcroix Nicolas N Bernaudin Myriam M Touzani Omar O Valable Samuel S

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 20160101 6

The alleviation of hypoxia in glioblastoma with carbogen to improve treatment has met with limited success. Our hypothesis is that the eventual benefits of carbogen depend on the capacity for vasodilation. We examined, with MRI, changes in fractional cerebral blood volume, blood oxygen saturation, and blood oxygenation level dependent signals in response to carbogen. The analyses were performed in two xenograft models of glioma (U87 and U251) recognized to have different vascular patterns. Carbo ...[more]

PMID: 27496553

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Project description:Background:To evaluate the association of multiparametric and multiregional MRI-features with key molecular characteristics in patients with newly-diagnosed glioblastoma. Methods:Retrospective data evaluation was approved by the local ethics committee of the University of Heidelberg (ethics approval number: S-320/2012) and informed consent was waived. Preoperative MRI-features were correlated with key molecular characteristics within a single-institutional cohort of 152 patients with newly-diagnosed glioblastoma. Preoperative MRI-features (n=31) included multiparametric (anatomical, diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast enhancing and non-enhancing FLAIR-hyperintense) information with (histogram) quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow / volume (CBF / CBV) and intratumoral susceptibility signals. Molecular characteristics determined with the Illumina Infinium HumanMethylation450 array included global DNA-methylation subgroups (e.g. mesenchymal (MES), RTK I “PGFRA”, RTK II “classic”), MGMT-promoter methylation status and hallmark copy-number-variations (EGFR-, PDGFRA-, MDM4- and CDK4-amplification; PTEN-, CDKN2A-, NF1- and RB1-loss). Univariate analyses (voxel-lesion-symptom-mapping for tumor location, Wilcoxon-test for all other MRI-features) as well as machine-learning models were applied to study the strength of association and discriminative value of MRI-features for predicting underlying molecular characteristics. Results: There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted p>0.05 each). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, both demonstrating increased nrCBV and nrCBF values (performance of these parameters, as assessed by the area under the ROC curve ranged from 63 to 69%, FDR-adjusted p<0.05, respectively). Subjecting all MRI-features to machine-learning-based classification allowed to predict EGFR amplification status and the RTK II “classic” GB subgroup with a moderate, yet significantly greater accuracy (63% for EGFR [p<0.01] and 61% for RTK II [p=0.01]) than the prediction by chance, whereas prediction accuracy for all other molecular parameters was non-significant (p>0.05, all models). Conclusions: In summary, we found univariate associations between established MRI-features and molecular characteristics, however not of sufficient strength to allow the generation of machine-learning classification models for reliable and clinically meaningful prediction of the assessed molecular characteristics in patients with newly-diagnosed glioblastoma.

Project description:PurposeConstruction of radiomics models for the individualized estimation of multiple survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI that could facilitate clinical decision-making for GBM patients.Materials and methodsA total of 134 eligible GBM patients were selected from The Cancer Genome Atlas. These patients were separated into the long-term and short-term survival groups according to the median of individual survival indicators: overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). Then, the patients were divided into a training set and a validation set in a ratio of 2:1. Radiomics features (n = 5,152) were extracted from multiple regions of the GBM using multiparametric MRI. Then, radiomics signatures that are related to the three survival indicators were respectively constructed using the analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) regression for each patient in the training set. Based on a Cox proportional hazards model, the radiomics model was further constructed by combining the signature and clinical risk factors.ResultsThe constructed radiomics model showed a promising discrimination ability to differentiate in the training set and validation set of GBM patients with survival indicators of OS, PFS, and DSS. Both the four MRI modalities and five tumor subregions have different effects on the three survival indicators of GBM. The favorable calibration and decision curve analysis indicated the clinical decision value of the radiomics model. The performance of models of the three survival indicators was different but excellent; the best model achieved C indexes of 0.725, 0.677, and 0.724, respectively, in the validation set.ConclusionOur results show that the proposed radiomics models have favorable predictive accuracy on three survival indicators and can provide individualized probabilities of survival stratification for GBM patients by using multiparametric and multiregional MRI features.

Project description:BackgroundEpidermal growth factor receptor variant III (EGFRvIII) is a driver mutation and potential therapeutic target in glioblastoma. Non-invasive in vivo EGFRvIII determination, using clinically acquired multiparametric MRI sequences, could assist in assessing spatial heterogeneity related to EGFRvIII, currently not captured via single-specimen analyses. We hypothesize that integration of subtle, yet distinctive, quantitative imaging/radiomic patterns using machine learning may lead to non-invasively determining molecular characteristics, and particularly the EGFRvIII mutation.MethodsWe integrated diverse imaging features, including the tumor's spatial distribution pattern, via support vector machines, to construct an imaging signature of EGFRvIII. This signature was evaluated in independent discovery (n = 75) and replication (n = 54) cohorts of de novo glioblastoma, and compared with the EGFRvIII status obtained through an assay based on next-generation sequencing.ResultsThe cross-validated accuracy of the EGFRvIII signature in classifying the mutation status in individual patients of the independent discovery and replication cohorts was 85.3% (specificity = 86.3%, sensitivity = 83.3%, area under the curve [AUC] = 0.85) and 87% (specificity = 90%, sensitivity = 78.6%, AUC = 0.86), respectively. The signature was consistent with EGFRvIII+ tumors having increased neovascularization and cell density, as well as a distinctive spatial pattern involving relatively more frontal and parietal regions compared with EGFRvIII- tumors.ConclusionsAn imaging signature of EGFRvIII was found, revealing a complex, yet distinct macroscopic glioblastoma phenotype. By non-invasively capturing the tumor in its entirety, the proposed methodology can assist in evaluating the tumor's spatial heterogeneity, hence overcoming common spatial sampling limitations of tissue-based analyses. This signature can preoperatively stratify patients for EGFRvIII-targeted therapies, and potentially monitor dynamic mutational changes during treatment.

Project description:Skeletal muscle perfusion and oxygenation are commonly evaluated using Doppler ultrasound and near-infrared spectroscopy (NIRS) techniques. However, a recently developed magnetic resonance imaging (MRI) sequence, termed PIVOT, permits the simultaneous collection of skeletal muscle perfusion and T2* (an index of skeletal muscle oxygenation).PurposeTo determine the level of agreement between PIVOT, Doppler ultrasound, and NIRS-based assessments of skeletal muscle perfusion and oxygenation.MethodsTwelve healthy volunteers (8 females, 25 ± 3 years, 170 ± 11 cm, 71.5 ± 8.0 kg) performed six total reactive hyperemia protocols. During three of these reactive hyperemia protocols, Tissue Saturation Index (TSI) and oxygenated hemoglobin (O2Hb) were recorded from the medial gastrocnemius (MG) and tibialis anterior (TA), and blood flow velocity was recorded from the popliteal artery (BFvpop) via Doppler Ultrasound. The other three trials were performed inside the bore of a 3 T MRI scanner, and the PIVOT sequence was used to assess perfusion (PIVOTperf) and oxygenation (T2*) of the medial gastrocnemius and tibialis anterior muscles. Positive incremental areas under the curve (iAUC) and times to peak (TTP) were calculated for each variable, and the level of agreement between collection methods was evaluated via Bland-Altman analyses and Spearman's Rho correlation analyses.ResultsThe only significant bivariate relationships observed were between the T2* vs. TSI iAUC and PIVOTperf vs. BFvpop values recorded from the MG. Significant mean differences were observed for all comparisons (all P ≤ 0.038), and significant proportional biases were observed for the PIVOTperf vs. tHb TTP (R2 = 0.848, P < 0.001) and T2* vs. TSI TTP comparisons in the TA (R2 = 0.488, P = 0.011), and the PIVOTperf vs. BFvpop iAUC (R2 = 0.477, P = 0.013) and time to peak (R2 = 0.851, P < 0.001) comparisons in the MG.ConclusionsOur findings suggest that the PIVOT technique has, at best, a moderate level of agreement with Doppler ultrasound and NIRS assessment methods and is subject to significant proportional bias. These findings do not challenge the accuracy of either measurement technique but instead reflect differences in the vascular compartments, sampling volumes, and parameters being evaluated.

Dataset Information

Carbogen-induced increases in tumor oxygenation depend on the vascular status of the tumor: A multiparametric MRI study in two rat glioblastoma models.

Publications

Carbogen-induced increases in tumor oxygenation depend on the vascular status of the tumor: A multiparametric MRI study in two rat glioblastoma models.

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